We follow the same experimental setup as [1]}.
We build a graph by connecting from sensors \(i\)  to \(j\)  if the road network distance from \(i\)  to \(j\)  is small [1]}. Since road network distance is used, the distance from \(i\)  to \(j\)  may be different from the distance from \(j\)  to \(i\) , making the adjacency matrix \(E\)  asymmetric.
We use 70% of the data for training, 10% for validation and 20% for testing. We consider three metrics to evaluate the prediction accuracy: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE). We consider the same forecasting setting as [1]} where we use \(l=12\)  past observations to predict the next \(p=12\)  steps ahead and report the errors at three different intervals (15, 30, and 60 mins) and also the average errors over \(p=12\)  steps.
